Info
Where raw data meets algorithmic reality. Gradient Descent is the essential listen for the modern data scientist, ML engineer, and quant. This isn't Statistics 101; this is the application. We...
show more
Where raw data meets algorithmic reality. Gradient Descent is the essential listen for the modern data scientist, ML engineer, and quant. This isn't Statistics 101; this is the application. We dive into the probabilistic models that power AI, explore Bayesian inference for real-time decision making, and debate the ethics of algorithmic bias. With a mix of solo deep-dives and interviews with industry quants, we focus on the code, the distributions, and the edge cases that break the system. If you live in Python and breathe confidence intervals, this is your new favorite feed.
show less
Episodes & Posts
Episodes
Posts
Transcribed
20 AUG 2026 · See gradient descent in action through linear regression. Learn how a model adjusts its parameters step by step to reduce prediction error and find a better fit.
Transcribed
20 AUG 2026 · Not all gradient descent methods learn the same way. Compare batch, stochastic, and mini-batch gradient descent and understand when each approach is most useful.
Transcribed
20 AUG 2026 · The learning rate determines how quickly a model learns. Discover what happens when the learning rate is too large, too small, or just right.
Transcribed
20 AUG 2026 · Explore the mathematical foundation of gradient descent, including cost functions, derivatives, gradients, and how these concepts guide a model toward better solutions.
Transcribed
20 AUG 2026 · What is gradient descent, and why is it so important in machine learning? This episode introduces the core concept and explains how algorithms use it to minimize errors and improve predictions.
Transcribed
24 SEP 2016 · Fashion is more than clothes. It can reflect identity, culture, confidence, and social change. In this episode, we explore what our style choices reveal about who we are and how we want to be seen.
Transcribed
10 JUL 2014 · Cheap trends come with hidden costs. We take an honest look at fast fashion, from its impact on workers and the environment to why consumers continue to embrace the trend cycle.
Where raw data meets algorithmic reality. Gradient Descent is the essential listen for the modern data scientist, ML engineer, and quant. This isn't Statistics 101; this is the application. We...
show more
Where raw data meets algorithmic reality. Gradient Descent is the essential listen for the modern data scientist, ML engineer, and quant. This isn't Statistics 101; this is the application. We dive into the probabilistic models that power AI, explore Bayesian inference for real-time decision making, and debate the ethics of algorithmic bias. With a mix of solo deep-dives and interviews with industry quants, we focus on the code, the distributions, and the edge cases that break the system. If you live in Python and breathe confidence intervals, this is your new favorite feed.
show less
Information
| Author | Marthinusbaloyi |
| Organization | Marthinusbaloyi |
| Categories | Fashion & Beauty |
| Website | - |
| - |
Copyright 2026 - Spreaker Inc. an iHeartMedia Company
